Overview

Dataset statistics

Number of variables32
Number of observations124462
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.6 MiB
Average record size in memory123.0 B

Variable types

Categorical20
DateTime1
Text1
Numeric10

Alerts

metric7 is highly overall correlated with metric8High correlation
metric8 is highly overall correlated with metric7High correlation
failure is highly imbalanced (99.0%)Imbalance
D__Z1F1 is highly imbalanced (68.0%)Imbalance
D__Z1F2 is highly imbalanced (97.9%)Imbalance
MoW_5 is highly imbalanced (65.6%)Imbalance
metric2 is highly skewed (γ1 = 23.89959781)Skewed
metric3 is highly skewed (γ1 = 82.81730486)Skewed
metric4 is highly skewed (γ1 = 41.49760767)Skewed
metric7 is highly skewed (γ1 = 73.63460697)Skewed
metric8 is highly skewed (γ1 = 73.63460697)Skewed
metric9 is highly skewed (γ1 = 49.8928614)Skewed
metric2 has 118082 (94.9%) zerosZeros
metric3 has 115331 (92.7%) zerosZeros
metric4 has 115130 (92.5%) zerosZeros
metric7 has 123007 (98.8%) zerosZeros
metric8 has 123007 (98.8%) zerosZeros
metric9 has 97332 (78.2%) zerosZeros

Reproduction

Analysis started2023-06-22 08:10:17.598430
Analysis finished2023-06-22 08:10:33.068326
Duration15.47 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

failure
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
124356 
1
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Length

2023-06-22T16:10:33.118140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:33.207041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

date
Date

Distinct303
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
Minimum2015-01-01 00:00:00
Maximum2015-10-31 00:00:00
2023-06-22T16:10:33.285048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:33.387811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

device
Text

Distinct1169
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:33.544171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters995696
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowS1F01085
2nd rowW1F0Y13C
3rd rowW1F0XKWR
4th rowW1F0X7QX
5th rowW1F0X7PR
ValueCountFrequency (%)
z1f0kkn4 303
 
0.2%
w1f05x69 303
 
0.2%
z1f0qlc1 303
 
0.2%
w1f0g9t7 303
 
0.2%
w1f0fy92 303
 
0.2%
z1f0qk05 303
 
0.2%
z1f0ql3n 303
 
0.2%
z1f0q8rt 303
 
0.2%
s1f0h6jg 303
 
0.2%
w1f0feh7 303
 
0.2%
Other values (1159) 121432
97.6%
2023-06-22T16:10:33.791254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 191959
19.3%
F 138423
13.9%
0 91178
 
9.2%
S 76236
 
7.7%
W 54962
 
5.5%
Z 39870
 
4.0%
L 25055
 
2.5%
3 23980
 
2.4%
K 18752
 
1.9%
B 17804
 
1.8%
Other values (24) 317477
31.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 593520
59.6%
Decimal Number 402175
40.4%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 138423
23.3%
S 76236
12.8%
W 54962
 
9.3%
Z 39870
 
6.7%
L 25055
 
4.2%
K 18752
 
3.2%
B 17804
 
3.0%
R 17712
 
3.0%
J 17504
 
2.9%
G 17261
 
2.9%
Other values (13) 169941
28.6%
Decimal Number
ValueCountFrequency (%)
1 191959
47.7%
0 91178
22.7%
3 23980
 
6.0%
6 15877
 
3.9%
5 15299
 
3.8%
2 14010
 
3.5%
4 13676
 
3.4%
7 12221
 
3.0%
9 12038
 
3.0%
8 11937
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
ÿ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 593521
59.6%
Common 402175
40.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 138423
23.3%
S 76236
12.8%
W 54962
 
9.3%
Z 39870
 
6.7%
L 25055
 
4.2%
K 18752
 
3.2%
B 17804
 
3.0%
R 17712
 
3.0%
J 17504
 
2.9%
G 17261
 
2.9%
Other values (14) 169942
28.6%
Common
ValueCountFrequency (%)
1 191959
47.7%
0 91178
22.7%
3 23980
 
6.0%
6 15877
 
3.9%
5 15299
 
3.8%
2 14010
 
3.5%
4 13676
 
3.4%
7 12221
 
3.0%
9 12038
 
3.0%
8 11937
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 995695
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 191959
19.3%
F 138423
13.9%
0 91178
 
9.2%
S 76236
 
7.7%
W 54962
 
5.5%
Z 39870
 
4.0%
L 25055
 
2.5%
3 23980
 
2.4%
K 18752
 
1.9%
B 17804
 
1.8%
Other values (23) 317476
31.9%
None
ValueCountFrequency (%)
ÿ 1
100.0%

metric1
Real number (ℝ)

Distinct123846
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2238518 × 108
Minimum0
Maximum2.4414048 × 108
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:33.908631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12089879
Q161271448
median1.2279194 × 108
Q31.8330837 × 108
95-th percentile2.3188178 × 108
Maximum2.4414048 × 108
Range2.4414048 × 108
Interquartile range (IQR)1.2203692 × 108

Descriptive statistics

Standard deviation70459869
Coefficient of variation (CV)0.57572222
Kurtosis-1.1992931
Mean1.2238518 × 108
Median Absolute Deviation (MAD)61031656
Skewness-0.011091131
Sum1.5232305 × 1013
Variance4.9645931 × 1015
MonotonicityNot monotonic
2023-06-22T16:10:34.014227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165048912 26
 
< 0.1%
89196552 26
 
< 0.1%
57192360 26
 
< 0.1%
169490248 23
 
< 0.1%
169467344 15
 
< 0.1%
57180136 15
 
< 0.1%
89162648 15
 
< 0.1%
165040624 15
 
< 0.1%
12194976 15
 
< 0.1%
89179832 13
 
< 0.1%
Other values (123836) 124273
99.8%
ValueCountFrequency (%)
0 11
< 0.1%
2048 1
 
< 0.1%
2056 2
 
< 0.1%
2168 1
 
< 0.1%
3784 1
 
< 0.1%
4224 1
 
< 0.1%
4480 1
 
< 0.1%
4560 1
 
< 0.1%
8280 1
 
< 0.1%
8616 1
 
< 0.1%
ValueCountFrequency (%)
244140480 1
< 0.1%
244138600 1
< 0.1%
244136552 1
< 0.1%
244135688 1
< 0.1%
244133240 1
< 0.1%
244132936 1
< 0.1%
244132752 1
< 0.1%
244131712 1
< 0.1%
244129416 1
< 0.1%
244127840 1
< 0.1%

metric2
Real number (ℝ)

SKEWED  ZEROS 

Distinct560
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.97226
Minimum0
Maximum64968
Zeros118082
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:34.116243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum64968
Range64968
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2172.2043
Coefficient of variation (CV)13.664047
Kurtosis629.39847
Mean158.97226
Median Absolute Deviation (MAD)0
Skewness23.899598
Sum19786005
Variance4718471.7
MonotonicityNot monotonic
2023-06-22T16:10:34.214114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118082
94.9%
2344 281
 
0.2%
8 260
 
0.2%
24 254
 
0.2%
40 201
 
0.2%
4960 175
 
0.1%
424 169
 
0.1%
16 166
 
0.1%
88 152
 
0.1%
552 140
 
0.1%
Other values (550) 4582
 
3.7%
ValueCountFrequency (%)
0 118082
94.9%
8 260
 
0.2%
16 166
 
0.1%
24 254
 
0.2%
32 132
 
0.1%
40 201
 
0.2%
48 90
 
0.1%
55 1
 
< 0.1%
56 103
 
0.1%
64 26
 
< 0.1%
ValueCountFrequency (%)
64968 1
 
< 0.1%
64792 6
 
< 0.1%
64784 11
< 0.1%
64776 8
< 0.1%
64736 13
< 0.1%
64728 13
< 0.1%
64584 17
< 0.1%
64472 1
 
< 0.1%
64464 1
 
< 0.1%
62296 1
 
< 0.1%

metric3
Real number (ℝ)

SKEWED  ZEROS 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9255837
Minimum0
Maximum24929
Zeros115331
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:34.317962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum24929
Range24929
Interquartile range (IQR)0

Descriptive statistics

Standard deviation185.67616
Coefficient of variation (CV)18.706826
Kurtosis10492.25
Mean9.9255837
Median Absolute Deviation (MAD)0
Skewness82.817305
Sum1235358
Variance34475.638
MonotonicityNot monotonic
2023-06-22T16:10:34.418807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 115331
92.7%
1 3273
 
2.6%
2 749
 
0.6%
7 298
 
0.2%
34 293
 
0.2%
5 278
 
0.2%
21 269
 
0.2%
4 267
 
0.2%
9 262
 
0.2%
8 251
 
0.2%
Other values (38) 3191
 
2.6%
ValueCountFrequency (%)
0 115331
92.7%
1 3273
 
2.6%
2 749
 
0.6%
3 113
 
0.1%
4 267
 
0.2%
5 278
 
0.2%
7 298
 
0.2%
8 251
 
0.2%
9 262
 
0.2%
10 241
 
0.2%
ValueCountFrequency (%)
24929 4
 
< 0.1%
2693 179
0.1%
2112 5
 
< 0.1%
1331 240
0.2%
1326 5
 
< 0.1%
1162 1
 
< 0.1%
406 84
 
0.1%
382 5
 
< 0.1%
378 1
 
< 0.1%
377 6
 
< 0.1%

metric4
Real number (ℝ)

SKEWED  ZEROS 

Distinct115
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7412142
Minimum0
Maximum1666
Zeros115130
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:34.672990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum1666
Range1666
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.911388
Coefficient of variation (CV)13.158282
Kurtosis2467.3544
Mean1.7412142
Median Absolute Deviation (MAD)0
Skewness41.497608
Sum216715
Variance524.9317
MonotonicityNot monotonic
2023-06-22T16:10:34.768698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115130
92.5%
6 3680
 
3.0%
1 889
 
0.7%
2 710
 
0.6%
3 466
 
0.4%
12 453
 
0.4%
4 358
 
0.3%
10 294
 
0.2%
112 245
 
0.2%
5 231
 
0.2%
Other values (105) 2006
 
1.6%
ValueCountFrequency (%)
0 115130
92.5%
1 889
 
0.7%
2 710
 
0.6%
3 466
 
0.4%
4 358
 
0.3%
5 231
 
0.2%
6 3680
 
3.0%
7 174
 
0.1%
8 170
 
0.1%
9 45
 
< 0.1%
ValueCountFrequency (%)
1666 9
< 0.1%
1074 6
 
< 0.1%
1033 3
 
< 0.1%
841 1
 
< 0.1%
763 1
 
< 0.1%
533 1
 
< 0.1%
529 4
 
< 0.1%
521 6
 
< 0.1%
487 18
< 0.1%
486 15
< 0.1%

metric5
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.223474
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:34.872113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median10
Q312
95-th percentile58
Maximum98
Range97
Interquartile range (IQR)4

Descriptive statistics

Standard deviation15.944958
Coefficient of variation (CV)1.1210312
Kurtosis12.147989
Mean14.223474
Median Absolute Deviation (MAD)2
Skewness3.4831636
Sum1770282
Variance254.24168
MonotonicityNot monotonic
2023-06-22T16:10:34.972894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 22142
17.8%
9 13595
10.9%
11 12784
10.3%
10 11475
9.2%
7 11271
9.1%
12 9835
7.9%
6 8542
 
6.9%
13 6005
 
4.8%
14 3517
 
2.8%
5 3428
 
2.8%
Other values (50) 21868
17.6%
ValueCountFrequency (%)
1 173
 
0.1%
2 203
 
0.2%
3 815
 
0.7%
4 933
 
0.7%
5 3428
 
2.8%
6 8542
 
6.9%
7 11271
9.1%
8 22142
17.8%
9 13595
10.9%
10 11475
9.2%
ValueCountFrequency (%)
98 224
 
0.2%
95 672
0.5%
94 224
 
0.2%
92 448
0.4%
91 215
 
0.2%
90 357
0.3%
89 224
 
0.2%
78 224
 
0.2%
70 224
 
0.2%
68 448
0.4%

metric6
Real number (ℝ)

Distinct44809
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260148.77
Minimum8
Maximum689161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:35.072835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q1221447.25
median249794
Q3310234.25
95-th percentile443124.6
Maximum689161
Range689153
Interquartile range (IQR)88787

Descriptive statistics

Standard deviation99150.928
Coefficient of variation (CV)0.38113165
Kurtosis1.9082846
Mean260148.77
Median Absolute Deviation (MAD)35370
Skewness-0.37496442
Sum3.2378636 × 1010
Variance9.8309065 × 109
MonotonicityNot monotonic
2023-06-22T16:10:35.170354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 777
 
0.6%
44 708
 
0.6%
27 636
 
0.5%
26 520
 
0.4%
29 441
 
0.4%
36 337
 
0.3%
35 290
 
0.2%
52 282
 
0.2%
45 246
 
0.2%
28 216
 
0.2%
Other values (44799) 120009
96.4%
ValueCountFrequency (%)
8 19
 
< 0.1%
9 172
0.1%
12 51
 
< 0.1%
18 36
 
< 0.1%
19 30
 
< 0.1%
20 6
 
< 0.1%
21 58
 
< 0.1%
23 71
 
0.1%
24 123
0.1%
25 184
0.1%
ValueCountFrequency (%)
689161 1
< 0.1%
689062 1
< 0.1%
689035 1
< 0.1%
688964 1
< 0.1%
688952 2
< 0.1%
687901 1
< 0.1%
687802 1
< 0.1%
687775 1
< 0.1%
687706 1
< 0.1%
687694 2
< 0.1%

metric7
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29144639
Minimum0
Maximum832
Zeros123007
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:35.265158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum832
Range832
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.4314916
Coefficient of variation (CV)25.498658
Kurtosis6897.9193
Mean0.29144639
Median Absolute Deviation (MAD)0
Skewness73.634607
Sum36274
Variance55.227068
MonotonicityNot monotonic
2023-06-22T16:10:35.345352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 123007
98.8%
8 792
 
0.6%
16 397
 
0.3%
24 65
 
0.1%
48 36
 
< 0.1%
32 34
 
< 0.1%
128 23
 
< 0.1%
40 20
 
< 0.1%
176 20
 
< 0.1%
6 13
 
< 0.1%
Other values (18) 55
 
< 0.1%
ValueCountFrequency (%)
0 123007
98.8%
6 13
 
< 0.1%
8 792
 
0.6%
16 397
 
0.3%
22 2
 
< 0.1%
24 65
 
0.1%
32 34
 
< 0.1%
40 20
 
< 0.1%
48 36
 
< 0.1%
56 6
 
< 0.1%
ValueCountFrequency (%)
832 2
 
< 0.1%
744 1
 
< 0.1%
736 4
 
< 0.1%
496 1
 
< 0.1%
424 1
 
< 0.1%
312 5
 
< 0.1%
272 2
 
< 0.1%
240 1
 
< 0.1%
216 1
 
< 0.1%
176 20
< 0.1%

metric8
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29144639
Minimum0
Maximum832
Zeros123007
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:35.429288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum832
Range832
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.4314916
Coefficient of variation (CV)25.498658
Kurtosis6897.9193
Mean0.29144639
Median Absolute Deviation (MAD)0
Skewness73.634607
Sum36274
Variance55.227068
MonotonicityNot monotonic
2023-06-22T16:10:35.510099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 123007
98.8%
8 792
 
0.6%
16 397
 
0.3%
24 65
 
0.1%
48 36
 
< 0.1%
32 34
 
< 0.1%
128 23
 
< 0.1%
40 20
 
< 0.1%
176 20
 
< 0.1%
6 13
 
< 0.1%
Other values (18) 55
 
< 0.1%
ValueCountFrequency (%)
0 123007
98.8%
6 13
 
< 0.1%
8 792
 
0.6%
16 397
 
0.3%
22 2
 
< 0.1%
24 65
 
0.1%
32 34
 
< 0.1%
40 20
 
< 0.1%
48 36
 
< 0.1%
56 6
 
< 0.1%
ValueCountFrequency (%)
832 2
 
< 0.1%
744 1
 
< 0.1%
736 4
 
< 0.1%
496 1
 
< 0.1%
424 1
 
< 0.1%
312 5
 
< 0.1%
272 2
 
< 0.1%
240 1
 
< 0.1%
216 1
 
< 0.1%
176 20
< 0.1%

metric9
Real number (ℝ)

SKEWED  ZEROS 

Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.454524
Minimum0
Maximum18701
Zeros97332
Zeros (%)78.2%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:35.601930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum18701
Range18701
Interquartile range (IQR)0

Descriptive statistics

Standard deviation191.45017
Coefficient of variation (CV)15.371938
Kurtosis4049.1503
Mean12.454524
Median Absolute Deviation (MAD)0
Skewness49.892861
Sum1550115
Variance36653.169
MonotonicityNot monotonic
2023-06-22T16:10:35.699850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97332
78.2%
1 9434
 
7.6%
2 3721
 
3.0%
3 2327
 
1.9%
4 1395
 
1.1%
6 797
 
0.6%
7 774
 
0.6%
5 735
 
0.6%
8 733
 
0.6%
10 640
 
0.5%
Other values (56) 6574
 
5.3%
ValueCountFrequency (%)
0 97332
78.2%
1 9434
 
7.6%
2 3721
 
3.0%
3 2327
 
1.9%
4 1395
 
1.1%
5 735
 
0.6%
6 797
 
0.6%
7 774
 
0.6%
8 733
 
0.6%
9 335
 
0.3%
ValueCountFrequency (%)
18701 5
 
< 0.1%
10137 4
 
< 0.1%
7226 5
 
< 0.1%
2794 6
 
< 0.1%
2637 84
0.1%
2522 179
0.1%
2270 5
 
< 0.1%
2269 1
 
< 0.1%
1864 5
 
< 0.1%
1165 118
0.1%

DaysRunning
Real number (ℝ)

Distinct303
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.09326
Minimum0
Maximum303
Zeros1169
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:10:35.806210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q139
median85
Q3167
95-th percentile250
Maximum303
Range303
Interquartile range (IQR)128

Descriptive statistics

Standard deviation78.346307
Coefficient of variation (CV)0.74549318
Kurtosis-0.78685584
Mean105.09326
Median Absolute Deviation (MAD)58
Skewness0.55410834
Sum13080117
Variance6138.1438
MonotonicityNot monotonic
2023-06-22T16:10:35.900470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1169
 
0.9%
2 1168
 
0.9%
1 1168
 
0.9%
3 1167
 
0.9%
4 1166
 
0.9%
5 1059
 
0.9%
6 799
 
0.6%
7 757
 
0.6%
8 757
 
0.6%
10 756
 
0.6%
Other values (293) 114496
92.0%
ValueCountFrequency (%)
0 1169
0.9%
1 1168
0.9%
2 1168
0.9%
3 1167
0.9%
4 1166
0.9%
5 1059
0.9%
6 799
0.6%
7 757
0.6%
8 757
0.6%
9 756
0.6%
ValueCountFrequency (%)
303 31
< 0.1%
302 31
< 0.1%
301 31
< 0.1%
299 31
< 0.1%
298 32
< 0.1%
297 32
< 0.1%
296 32
< 0.1%
295 32
< 0.1%
294 69
0.1%
293 69
0.1%

D__S1F0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
91304 
1
33158 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

Length

2023-06-22T16:10:35.989779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:36.072040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

Most occurring characters

ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

D__S1F1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
102775 
1
21687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

Length

2023-06-22T16:10:36.141785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:36.224214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

Most occurring characters

ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

D__W1F0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
101178 
1
23284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

Length

2023-06-22T16:10:36.294648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:36.382234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

D__W1F1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
104488 
1
19974 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

Length

2023-06-22T16:10:36.452034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:36.535525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

D__Z1F0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
105602 
1
18860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

Length

2023-06-22T16:10:36.604299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:36.686698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

D__Z1F1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
117214 
1
 
7248

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

Length

2023-06-22T16:10:36.755864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:36.835479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

D__Z1F2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
124211 
1
 
251

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

Length

2023-06-22T16:10:36.903257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:36.984467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

DoW_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106607 
1
17855 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

Length

2023-06-22T16:10:37.052577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:37.133937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

DoW_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106928 
1
17534 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

Length

2023-06-22T16:10:37.203128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:37.283306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

DoW_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
107326 
1
17136 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

Length

2023-06-22T16:10:37.353545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:37.434614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

DoW_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106321 
1
18141 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

Length

2023-06-22T16:10:37.503387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:37.585521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

Most occurring characters

ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

DoW_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106422 
1
18040 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

Length

2023-06-22T16:10:37.653794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:37.735994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

DoW_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106565 
1
17897 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

Length

2023-06-22T16:10:37.805431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:37.887460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

Most occurring characters

ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

DoW_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106603 
1
17859 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

Length

2023-06-22T16:10:37.956604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:38.039003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

MoW_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
91935 
1
32527 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

Length

2023-06-22T16:10:38.108241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:38.190140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

Most occurring characters

ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

MoW_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
94933 
1
29529 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

Length

2023-06-22T16:10:38.258874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:38.340280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

Most occurring characters

ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

MoW_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
96474 
1
27988 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

Length

2023-06-22T16:10:38.411187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:38.494168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

Most occurring characters

ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

MoW_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
98025 
1
26437 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

Length

2023-06-22T16:10:38.564270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:38.645174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

Most occurring characters

ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

MoW_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
116481 
1
 
7981

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Length

2023-06-22T16:10:38.715710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:10:38.795443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Interactions

2023-06-22T16:10:31.205964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:23.543270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.321549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.172119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.005817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.851771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.691457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:28.713138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.527611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.339959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.292680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:23.615026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.406390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.255115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.091810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.937626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.781286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:28.797336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.609626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.425782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.379349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:23.682236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.493234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.338218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.176606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.023459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.871195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:28.880681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.692299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.515014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.461782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:23.747961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.576055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.419799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.258993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.106159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.957008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:28.961269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.772935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.600191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.549143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:23.818418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.662206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.502698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.342472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.190168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:28.046215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.042787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.853618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.688122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.631425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:23.898133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.743934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.585255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.426228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.271903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:28.132377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.122720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.933815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.771685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.722167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:23.988488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.836808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.676026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.515438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.361056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:28.223258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.208901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.020344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.864812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.801734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.067026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.916156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.754871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.595645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.440491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:28.307351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.284691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.094899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.946165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.883332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.146392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.996872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.832422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.676656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.518704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:28.526322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.359087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.170544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.029532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.971866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:24.234867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.085050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:25.920238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:26.765565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:27.607428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:28.623659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:29.445292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:30.255411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:10:31.117489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-22T16:10:38.878889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
metric1metric2metric3metric4metric5metric6metric7metric8metric9DaysRunningfailureD__S1F0D__S1F1D__W1F0D__W1F1D__Z1F0D__Z1F1D__Z1F2DoW_0DoW_1DoW_2DoW_3DoW_4DoW_5DoW_6MoW_1MoW_2MoW_3MoW_4MoW_5
metric11.000-0.0010.0020.002-0.005-0.003-0.002-0.002-0.003-0.0050.0090.0030.0030.0000.0040.0040.0030.0000.0060.0010.0000.0080.0050.0020.0090.0000.0000.0000.0000.002
metric2-0.0011.000-0.0190.225-0.027-0.0780.1090.109-0.029-0.0210.0960.0280.0240.0400.0620.0240.0330.0000.0000.0000.0000.0000.0000.0000.0000.0070.0070.0050.0020.005
metric30.002-0.0191.0000.1210.1070.070-0.010-0.0100.3900.0020.0000.0640.0170.0180.0160.0160.0090.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.000
metric40.0020.2250.1211.000-0.0210.0120.1630.1630.049-0.0210.1120.0220.0210.0250.0840.0190.0090.0000.0010.0000.0000.0000.0000.0000.0000.0200.0030.0080.0040.000
metric5-0.005-0.0270.107-0.0211.0000.083-0.020-0.0200.034-0.0100.0070.1270.1800.1890.1330.2080.1180.0370.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0060.008
metric6-0.003-0.0780.0700.0120.0831.000-0.016-0.0160.0900.1860.0120.1870.2210.1810.4130.3030.2040.1070.0000.0000.0140.0050.0000.0000.0000.0880.0300.0300.0340.035
metric7-0.0020.109-0.0100.163-0.020-0.0161.0001.000-0.0180.0190.1140.0170.0090.0000.0160.0080.0410.0000.0000.0030.0000.0000.0000.0000.0000.0100.0000.0050.0020.017
metric8-0.0020.109-0.0100.163-0.020-0.0161.0001.000-0.0180.0190.1140.0170.0090.0000.0160.0080.0410.0000.0000.0030.0000.0000.0000.0000.0000.0100.0000.0050.0020.017
metric9-0.003-0.0290.3900.0490.0340.090-0.018-0.0181.000-0.0200.0000.0790.0210.0220.0200.0200.0370.0000.0000.0000.0000.0000.0000.0000.0000.0170.0020.0010.0000.000
DaysRunning-0.005-0.0210.002-0.021-0.0100.1860.0190.019-0.0201.0000.0130.0650.0540.0680.0370.0330.0440.0410.0440.0460.0540.0400.0370.0410.0440.1010.0480.0370.0610.129
failure0.0090.0960.0000.1120.0070.0120.1140.1140.0000.0131.0000.0040.0080.0040.0000.0000.0000.0000.0080.0000.0000.0040.0000.0040.0080.0030.0000.0050.0000.000
D__S1F00.0030.0280.0640.0220.1270.1870.0170.0170.0790.0650.0041.0000.2770.2890.2630.2550.1500.0270.0000.0000.0000.0000.0000.0000.0000.0080.0000.0010.0000.009
D__S1F10.0030.0240.0170.0210.1800.2210.0090.0090.0210.0540.0080.2771.0000.2200.2010.1940.1140.0200.0000.0000.0020.0000.0000.0000.0000.0130.0020.0080.0000.000
D__W1F00.0000.0400.0180.0250.1890.1810.0000.0000.0220.0680.0040.2890.2201.0000.2100.2030.1190.0210.0000.0000.0030.0000.0000.0000.0000.0150.0020.0070.0020.001
D__W1F10.0040.0620.0160.0840.1330.4130.0160.0160.0200.0370.0000.2630.2010.2101.0000.1850.1090.0190.0000.0000.0010.0000.0000.0000.0000.0110.0010.0050.0000.000
D__Z1F00.0040.0240.0160.0190.2080.3030.0080.0080.0200.0330.0000.2550.1940.2030.1851.0000.1050.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0010.007
D__Z1F10.0030.0330.0090.0090.1180.2040.0410.0410.0370.0440.0000.1500.1140.1190.1090.1051.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
D__Z1F20.0000.0000.0000.0000.0370.1070.0000.0000.0000.0410.0000.0270.0200.0210.0190.0190.0101.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
DoW_00.0060.0000.0000.0010.0000.0000.0000.0000.0000.0440.0080.0000.0000.0000.0000.0000.0000.0001.0000.1660.1630.1690.1680.1680.1670.0000.0000.0000.0010.012
DoW_10.0010.0000.0000.0000.0000.0000.0030.0030.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.1661.0000.1620.1670.1670.1660.1660.0000.0000.0000.0040.013
DoW_20.0000.0000.0000.0000.0000.0140.0000.0000.0000.0540.0000.0000.0020.0030.0010.0000.0000.0000.1630.1621.0000.1650.1640.1640.1630.0040.0040.0070.0070.024
DoW_30.0080.0000.0000.0000.0000.0050.0000.0000.0000.0400.0040.0000.0000.0000.0000.0000.0000.0000.1690.1670.1651.0000.1700.1690.1690.0000.0050.0000.0040.025
DoW_40.0050.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.1680.1670.1640.1701.0000.1690.1680.0000.0000.0050.0040.025
DoW_50.0020.0000.0000.0000.0000.0000.0000.0000.0000.0410.0040.0000.0000.0000.0000.0000.0000.0000.1680.1660.1640.1690.1691.0000.1680.0000.0000.0000.0070.011
DoW_60.0090.0000.0000.0000.0000.0000.0000.0000.0000.0440.0080.0000.0000.0000.0000.0000.0000.0000.1670.1660.1630.1690.1680.1681.0000.0000.0000.0000.0010.012
MoW_10.0000.0070.0090.0200.0120.0880.0100.0100.0170.1010.0030.0080.0130.0150.0110.0000.0000.0000.0000.0000.0040.0000.0000.0000.0001.0000.3320.3200.3090.156
MoW_20.0000.0070.0000.0030.0000.0300.0000.0000.0020.0480.0000.0000.0020.0020.0010.0000.0000.0000.0000.0000.0040.0050.0000.0000.0000.3321.0000.3000.2900.146
MoW_30.0000.0050.0000.0080.0000.0300.0050.0050.0010.0370.0050.0010.0080.0070.0050.0030.0000.0000.0000.0000.0070.0000.0050.0000.0000.3200.3001.0000.2800.141
MoW_40.0000.0020.0000.0040.0060.0340.0020.0020.0000.0610.0000.0000.0000.0020.0000.0010.0000.0000.0010.0040.0070.0040.0040.0070.0010.3090.2900.2801.0000.136
MoW_50.0020.0050.0000.0000.0080.0350.0170.0170.0000.1290.0000.0090.0000.0010.0000.0070.0000.0000.0120.0130.0240.0250.0250.0110.0120.1560.1460.1410.1361.000

Missing values

2023-06-22T16:10:32.136548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-22T16:10:32.653912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

failuredatedevicemetric1metric2metric3metric4metric5metric6metric7metric8metric9DaysRunningD__S1F0D__S1F1D__W1F0D__W1F1D__Z1F0D__Z1F1D__Z1F2DoW_0DoW_1DoW_2DoW_3DoW_4DoW_5DoW_6MoW_1MoW_2MoW_3MoW_4MoW_5
002015-01-01S1F0108521563067255052640743800701000000000100010000
102015-01-01W1F0Y13C234318640000418577200300010000000100010000
202015-01-01W1F0XKWR8966070400073000000010000000100010000
302015-01-01W1F0X7QX1620134560001221768600000010000000100010000
402015-01-01W1F0X7PR13138392000919134300000010000000100010000
502015-01-01W1F0X7P2332248320001321696000000010000000100010000
602015-01-01W1F0X711185386320001521759000000010000000100010000
702015-01-01W1F0Y2PY132033080000518205000000010000000100010000
802015-01-01W1F0X70N1655835520001318628800000010000000100010000
902015-01-01W1F0X6V01750313200001121351500000010000000100010000
failuredatedevicemetric1metric2metric3metric4metric5metric6metric7metric8metric9DaysRunningD__S1F0D__S1F1D__W1F0D__W1F1D__Z1F0D__Z1F1D__Z1F2DoW_0DoW_1DoW_2DoW_3DoW_4DoW_5DoW_6MoW_1MoW_2MoW_3MoW_4MoW_5
12445202015-10-31S1F0GPXY524465600001135018000103031000000000001000001
12445302015-10-31S1F0GGPP136252184021120123595820003031000000000001000001
12445402015-10-31S1F0GCED1832246086479207113394710003031000000000001000001
12445502015-10-31S1F0FP0C132684064000123542190003031000000000001000001
12445602015-10-31S1F0FGBQ152873000000123075730003031000000000001000001
12445702015-10-31S1F0EGMT177545088001211346829323203031000000000001000001
12445802015-10-31S1F0E9EP1605007440012113520640003031000000000001000001
12445902015-10-31Z1F0QL3N62018104000123569370003030000100000001000001
12446002015-10-31W1F0FY9281032808016153536610013030010000000001000001
12446102015-10-31Z1F0QLC132117368000103508400003030000100000001000001